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1. Cognitive and Decision-Making Model
1. Cognitive and Decision-Making Model
How do people perceive the world, remember information, reason through problems, and make decisions? Our research develops and applies cognitive and decision-making models to understand the mental mechanisms underlying these processes. By combining mathematical and computational approaches, we investigate how people navigate challenging problems, and explore ways to support and enhance their performance.
Representative Publications:
Chen, Y., Fang, J., Cesario, J., Liu, T., & Pleskac, T. (2025). Comparing One-Boundary and Two-Boundary Evidence Accumulation Models for Go/No-Go Processes: An Application to the Decision to Shoot. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 47).
Chen, Y., Peruggia, M., & Van Zandt, T. (2022). Mutual interference in working memory updating: A hierarchical Bayesian model. Journal of Mathematical Psychology, 111, 102706
2. Methods for Data-Limited Research
2. Methods for Data-Limited Research
In an ideal world, researchers could always collect sufficient data to accurately measure the psychological constructs of interest. In reality, however, empirical data often face constraints such as small sample sizes, limited observations per participant, and confounding factors. These limitations can undermine the utility of data and the robustness of conclusions. Our research seeks to use advanced statistical and computational methods to help extract meaningful information from limited data, improving their value for psychological research. These methods include Bayesian hierarchical modeling, machine learning, and psychometrics.
Representative Publications:
Chen, Y., Daly, H. R., Pitt, M. A., & Van Zandt, T. (2024). Assessing the distortions introduced when calculating d’: A simulation approach. Behavior Research Methods, 56(7), 7728-7747.
Chen, Y., Breitborde, N. J., Peruggia, M., & Van Zandt, T. (2022). Understanding motivation with the progressive ratio task: a hierarchical Bayesian model. Computational Brain & Behavior, 5(1), 81-102.
3. From Models to Real-World Predictions
3. From Models to Real-World Predictions
With well-developed models in place, an important next step is to make them practical tools for real-world problems. Our research uses both group- and individual-level cognitive traits to generate insights and predictions in domains such as health, education, and everyday decision-making. Ultimately, we aim to use quantitative methods to support more effective assessment, intervention, and evidence-based policy making.
Representative Publications:
Chen, Y., Forbush, K. T., & Pleskac, T. J. (2025). Bayesian Graded Response Models for Eating-Disorder Risk Estimation Using Screening Data. Computational Brain & Behavior, 8(1), 92-110.
Christensen Pacella, K. A., Forbush, K. T., Chen, Y., Nation, M. B., Cushing, C. C., & Swinburne Romine, R. E. (2024). Negative Affect as a Mediator Between Exposure to Fitspiration and Thinspiration and Disordered Eating Behaviors: An Ecological Momentary Assessment Study. International Journal of Eating Disorders, 57(12), 2504-2515.
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